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Add IndoNLI dataset
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[ "@lhoestq thanks for the review! I've modified the labels to follow other NLI datasets.\r\nPlease review my change and let me know if I miss anything." ]
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This PR adds IndoNLI dataset, from https://aclanthology.org/2021.emnlp-main.821/
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nested sequence feature won't encode example if the first item of the outside sequence is an empty list
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[ "knock knock", "Hi, thanks for reporting! I've linked a PR that should fix the issue.", "I've checked the PR and it looks great, thanks a lot!" ]
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## Describe the bug As the title, nested sequence feature won't encode example if the first item of the outside sequence is an empty list. ## Steps to reproduce the bug ```python from datasets import Features, Sequence, ClassLabel features = Features({ 'x': Sequence(Sequence(ClassLabel(names=['a', 'b']))), }) print(features.encode_batch({ 'x': [ [['a'], ['b']], [[], ['b']], ] })) ``` ## Expected results print `{'x': [[[0], [1]], [[], ['1']]]}` ## Actual results print `{'x': [[[0], [1]], [[], ['b']]]}` ## Environment info - `datasets` version: 1.15.1 - Platform: Linux-5.13.0-21-generic-x86_64-with-glibc2.34 - Python version: 3.9.7 - PyArrow version: 6.0.0 ## Additional information I think the issue stems from [here](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/features/features.py#L847-L848).
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asserts replaced with exception for ``fingerprint.py``, ``search.py``, ``arrow_writer.py`` and ``metric.py``
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Addresses #3171 Fixes exception for ``fingerprint.py``, ``search.py``, ``arrow_writer.py`` and ``metric.py`` and modified tests
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Dataset object has no attribute `to_tf_dataset`
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[ "The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n```\r\n# upgrade transformers and datasets to latest versions\r\n!pip install --upgrade transformers\r\n!pip install --upgrade datasets\r\n```\r\n\r\nRegards!" ]
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I am following HuggingFace Course. I am at Fine-tuning a model. Link: https://huggingface.co/course/chapter3/2?fw=tf I use tokenize_function and `map` as mentioned in the course to process data. `# define a tokenize function` `def Tokenize_function(example):` ` return tokenizer(example['sentence'], truncation=True)` `# tokenize entire data` `tokenized_data = raw_data.map(Tokenize_function, batched=True)` I get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error. `# convert to TF dataset` `train_data = tokenized_data["train"].to_tf_dataset( ` ` columns = ['attention_mask', 'input_ids', 'token_type_ids'], ` ` label_cols = ['label'], ` ` shuffle = True, ` ` collate_fn = data_collator, ` ` batch_size = 8 ` `)` Output: `---------------------------------------------------------------------------` `AttributeError Traceback (most recent call last)` `/tmp/ipykernel_42/103099799.py in <module>` ` 1 # convert to TF dataset` `----> 2 train_data = tokenized_data["train"].to_tf_dataset( \` ` 3 columns = ['attention_mask', 'input_ids', 'token_type_ids'], \` ` 4 label_cols = ['label'], \` ` 5 shuffle = True, \` `AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'` When I look for `dir(tokenized_data["train"])`, there is no method or attribute in the name of `to_tf_dataset`. Why do I get this error? And how to clear this? Please help me.
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DataCollatorWithPadding: TypeError
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[ "\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> /tmp/ipykernel_42/1563280798.py in <module>\r\n> 1 checkpoint = 'bert-base-uncased'\r\n> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!" ]
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Hi, I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf). When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device. Input: ```checkpoint = 'bert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(checkpoint) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") ``` Output: ```--------------------------------------------------------------------------- TypeError Traceback (most recent call last) /tmp/ipykernel_42/1563280798.py in <module> 1 checkpoint = 'bert-base-uncased' 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint) ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt") TypeError: __init__() got an unexpected keyword argument 'return_tensors' ``` When I call `help` method, it too confirms that there is no argument `return_tensors`. Input: ``` help(DataCollatorWithPadding.__init__) ``` Output: ``` Help on function __init__ in module transformers.data.data_collator: __init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None ``` But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors. Where do I miss? Please help me.
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fix old_val typo in f-string
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This PR is to correct a typo in #3277 that @Carlosbogo revieled in a comment. Related closed issue : #3257 Sorry about that 😅.
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Add wikipedia tags
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Add the missing tags to the wikipedia dataset card. I also added the missing languages code in our language codes list. This should also fix the code snippet that is presented on the Hub to load the dataset: fix https://github.com/huggingface/datasets/issues/3292
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❓ Dataset loading script from Hugging Face Hub
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[ "Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n \"train\": \"train-v1.1.json\", # you can use a URL or a relative path from the python script to your file in the repository\r\n \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n ]\r\n```", "Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't", "Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly 🚀 \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro", "Great ! We'll take a look at the viewer to fix it", "@lhoestq I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n datasets = load_dataset(\r\n os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n f\"xfun.{data_args.lang}\",\r\n additional_langs=data_args.additional_langs,\r\n keep_in_memory=True,\r\n )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n downloaded_file = dl_manager.download_and_extract(\"https://guillaumejaume.github.io/FUNSD/dataset.zip\")\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/training_data/\"}\r\n ),\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/testing_data/\"}\r\n ),\r\n ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 652, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 975, in _prepare_split\r\n generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```", "Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset/testing_data\")` instead of `f\"downloaded_file}/dataset/testing_data/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example)", "Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can. We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have \"filepaths\".)\r\n" ]
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Hi there, I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below. Issues I have encountered: - Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations - Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this ```python load_dataset("pietrolesci/ag_news", name="my_configuration") ``` Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following ``` ag_news.py train.csv test.csv ``` and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub. Any suggestion is very much appreciated. Best, Pietro Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news BONUS: how can I make the data viewer work in this specific case? :)
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I_kwDODunzps4_F7TF
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Add option to find unique elements in nested sequences when calling `Dataset.unique`
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It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.
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I_kwDODunzps4_FjXp
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Agnews dataset viewer is not working
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[ "Hi ! Thanks for reporting\r\nWe've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)", "Hi @lhoestq, thanks for your feedback!" ]
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## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/ag_news Hi there, the `ag_news` dataset viewer is not working. Am I the one who added this dataset? No
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I_kwDODunzps4_E9Mz
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.map() cache is wrongfully reused - only happens when the mapping function is imported
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[ "Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account", "I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful." ]
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CONTRIBUTOR
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## Describe the bug When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified. The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411). I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably. ## Steps to reproduce the bug Create files `a.py` and `b.py`: ```python # a.py from datasets import load_dataset def main(): squad = load_dataset("squad") squad.map(mapping_func, batched=True) def mapping_func(examples): ID_LENGTH = 4 examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]] return examples if __name__ == "__main__": main() ``` ```python # b.py from datasets import load_dataset from a import mapping_func def main(): squad = load_dataset("squad") squad.map(mapping_func, batched=True) if __name__ == "__main__": main() ``` Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...". Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function. ## Expected results Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...". Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result. ## Workaround Put the mapping function inside a dummy class as a static method: ```python # a.py class MappingFuncClass: @staticmethod def mapping_func(examples): ID_LENGTH = 4 examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]] return examples ``` ```python # b.py from datasets import load_dataset from a import MappingFuncClass def main(): squad = load_dataset("squad") squad.map(MappingFuncClass.mapping_func, batched=True) if __name__ == "__main__": main() ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 4.0.1
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Fix temporary dataset_path creation for URIs related to remote fs
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[ "Hi ! Thanks for the fix :) \r\n\r\nI think this should be `extract_path_from_uri` 's responsibility to strip the extra `/` from a badly formatted path like `hdfs:///absolute/path` (or raise an error). Do you think you can simply do the changes in `extract_path_from_uri` ? This way this fix will be available for all the other parts of the lib that need to extract the inner path from an URI of a remote filesystem\r\n\r\nThen we can also keep your test cases but simply apply them to `extract_path_from_uri` instead", "Hi @lhoestq! No problem! Thanks for your interest! :)\r\n\r\nI think stripping the 3rd `/` in `hdfs:///absolute/path` inside `extract_path_from_uri` is not the solution. When I provide `hdfs:///absolute/path` to `extract_path_from_uri` we want `/absolute/path` to be returned, as it does now (at least in the case of URIs with `hdfs` schemas, for `s3` is different as it should start with a bucket name).\r\n\r\nThe problem comes in line 1041 in the original code below:\r\n\r\nhttps://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042\r\n\r\nLets assume the following parameters for line 1041 after `extract_path_from_uri` has removed the `hdfs` schema part and the `://` from `hdfs:///absolute/path`, and `get_temporary_cache_files_directory()` returns `/tmp/a1b2b3c4`, as it is shown below: \r\n\r\n```python\r\nsrc_dataset_path = '/absolute/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nAfter passing those paths to the `Path` object, `dataset_path` contains only `/absolute/path`; that is, it has lost the temporary directory path. This is because, when two (or more) absolute paths are passed to the `Path` function, only the last one is taken. However, if the contents of those variables are:\r\n\r\n```python\r\nsrc_dataset_path = 'relative/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nthen `dataset_path` contains `/tmp/a1b2b3c4/relative/path` as expected.\r\n\r\nAbsolute paths are allowed in hdfs URIs, so that's why I added the extra function `build_local_temp_path` in the PR; so in case the second argument is an absolute path, it still will create the correct absolute path by concatenating the temp dir and the path passed by converting it to a relative path (and it also works for windows paths too.) It also allows to add the tests, checking that the main combinations are ok.\r\n\r\nI've checked all the places where the result of `extract_path_from_uri` is used, and as far as I've seen this is the only place where it is concatenated with another possible absolute path, so no need to add `build_local_temp_path` anywhere else. \r\n" ]
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This aims to close #3295
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Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk
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[ "Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR" ]
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## Describe the bug When trying to build a temporary dataset path from a remote URI in this block of code: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042 the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`. ## Steps to reproduce the bug ```python dataset_path = "hdfs:///absolute/path" src_dataset_path = extract_path_from_uri(dataset_path) tmp_dir = get_temporary_cache_files_directory() dataset_path = Path(tmp_dir, src_dataset_path) print(dataset_path) ``` ## Expected results With the code above, we would expect a value in `dataset_path` similar to: `/tmp/tmpnwxyvao5/absolute/path` ## Actual results However, we get a `dataset_path` value like: `/absolute/path` This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041 returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments. ## Environment info - `datasets` version: 1.13.3 - Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 5.0.0
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1,057,495,473
I_kwDODunzps4_CBmx
3,294
Add Natural Adversarial Objects dataset
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## Adding a Dataset - **Name:** Natural Adversarial Objects (NAO) - **Description:** Natural Adversarial Objects (NAO) is a new dataset to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence. - **Paper:** https://arxiv.org/abs/2111.04204v1 - **Data:** https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8 - **Motivation:** interesting object detection dataset useful for miscclassifications cc @NielsRogge Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Pin version exclusion for Markdown
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As Markdown version 3.3.5 has a bug, it is better to exclude it in case the users have it previously installed in their environment. Related to #3289, #3286.
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Not able to load 'wikipedia' dataset
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[ "Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n![image](https://user-images.githubusercontent.com/42851186/142649237-45ba55c5-1a64-4c30-8692-2c8120572f92.png)\r\n\r\nThanks for reporting, I'm taking a look\r\n" ]
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## Describe the bug I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error. ## Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("wikipedia") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 339 "Config name is missing." 340 "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys()) --> 341 + "\nExample of usage:\n\t`{}`".format(example_of_usage) 342 ) 343 builder_config = self.BUILDER_CONFIGS[0] ValueError: Config name is missing. Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] Example of usage: `load_dataset('wikipedia', '20200501.aa')` I think the other parameter is missing in the load_dataset function that is not shown in the instruction.
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Use f-strings in the dataset scripts
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Uses f-strings to format the .py files in the dataset folder
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Make several audio datasets streamable
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[ "Reading FLAC (for `librispeech_asr`) works OK for me (`soundfile` version: `0.10.3`):\r\n```python\r\nIn [2]: ds = load_dataset(\"datasets/librispeech_asr/librispeech_asr.py\", \"clean\", streaming=True, split=\"train.100\")\r\n\r\nIn [3]: item = next(iter(ds))\r\n\r\nIn [4]: item.keys()\r\nOut[4]: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\n\r\nIn [5]: item[\"file\"]\r\nOut[5]: '374-180298-0000.flac'\r\n\r\nIn [6]: item[\"audio\"].keys()\r\nOut[6]: dict_keys(['path', 'array', 'sampling_rate'])\r\n\r\nIn [7]: item[\"audio\"][\"sampling_rate\"]\r\nOut[7]: 16000\r\n\r\nIn [8]: item[\"audio\"][\"path\"]\r\nOut[8]: '374-180298-0000.flac'\r\n\r\nIn [9]: item[\"audio\"][\"array\"].shape\r\nOut[9]: (232480,)\r\n```", "Oh cool ! I think this might have come from an issue with my local `soundfile` installation then", "I'll do `multilingual_librispeech` in a separate PR since it requires the data to be in another format (in particular separate the train/dev/test splits in different files)" ]
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<s>Needs https://github.com/huggingface/datasets/pull/3129 to be merged first</s> Make those audio datasets streamable: - [x] common_voice - [x] openslr - [x] vivos - [x] librispeech_asr <s>(still has some issues to read FLAC)</s> *actually it's ok* - [ ] <s>multilingual_librispeech (yet to be converted)</S> *TODO in a separate PR*
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Unpin markdown for build_docs now that it's fixed
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`markdown`'s bug has been fixed, so this PR reverts #3286
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Allow datasets with indices table when concatenating along axis=1
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Calls `flatten_indices` on the datasets with indices table in `concatenate_datasets` to fix issues when concatenating along `axis=1`. cc @lhoestq: I decided to flatten all the datasets instead of flattening all the datasets except the largest one in the end. The latter approach fails on the following example: ```python a = Dataset.from_dict({"a": [10, 20, 30, 40]}) b = Dataset.from_dict({"b": [10, 20, 30, 40, 50, 60]}) # largest dataset a = a.select([1, 2, 3]) b = b.select([1, 2, 3]) concatenate_datasets([a, b], axis=1) # fails at line concat_tables(...) because the real length of b's data is 6 and a's length is 3 after flattening (was 4 before flattening) ``` Also, it requires additional re-ordering of indices to prepare them for working with the indices table of the largest dataset. IMO not worth when we save only one `flatten_indices` call. (feel free to check the code of that approach at https://github.com/huggingface/datasets/commit/6acd10481c70950dcfdbfd2bab0bf0c74ad80bcb if you are interested) Fixes #3273
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Add The Pile dataset and PubMed Central subset
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Add: - The complete final version of The Pile dataset: "all" config - PubMed Central subset of The Pile: "pubmed_central" config Close #1675, close bigscience-workshop/data_tooling#74. CC: @StellaAthena, @lewtun
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Fix build_docs CI
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Because of https://github.com/Python-Markdown/markdown/issues/1196 we have to temporarily pin `markdown` to 3.3.4 for the docs to build without issues
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Add IEMOCAP dataset
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## Adding a Dataset - **Name:** IEMOCAP - **Description:** acted, multimodal and multispeaker database - **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf - **Data:** https://sail.usc.edu/iemocap/index.html - **Motivation:** Useful multimodal dataset cc @anton-l Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add VoxLingua107 dataset
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## Adding a Dataset - **Name:** VoxLingua107 - **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. - **Paper:** https://arxiv.org/abs/2011.12998 - **Data:** http://bark.phon.ioc.ee/voxlingua107/ - **Motivation:** Nice audio classification dataset cc @anton-l Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add Speech Commands dataset
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## Adding a Dataset - **Name:** Speech commands - **Description:** A Dataset for Limited-Vocabulary Speech Recognition - **Paper:** https://arxiv.org/abs/1804.03209 - **Data:** https://www.tensorflow.org/datasets/catalog/speech_commands, Available: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz - **Motivation:** Nice dataset for audio classification training cc @anton-l Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
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[ "Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.", "Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!", "Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?", "Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks", "The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)", "> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`" ]
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## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)* *The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.* ``` raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py ``` Am I the one who added this dataset ? No Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
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[Datasets] Improve Covost 2
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It's currently quite confusing to understand the manual data download instruction of Covost and not very user-friendly. Currenty the user has to: 1. Go on Common Voice website 2. Find the correct dataset which is **not** mentioned in the error message 3. Download it 4. Untar it 5. Create a language id folder (why? this folder does not exist in the `.tar` downloaded file) 6. pass the folder containing the created language id folder This PR improves this to: 1. Go on Common Voice website 2. Find the correct dataset which **is** mentioned in the error message 3. Download it 4. Untar it 5. pass the untared folder **Note**: This PR is not at all time-critical
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Fix bookcorpusopen RAM usage
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Each document is a full book, so the default arrow writer batch size of 10,000 is too big, and it can fill up RAM quickly before flushing the first batch on disk. I changed its batch size to 256 to use maximum 100MB of memory Fix #3167.
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Minor Typo Fix - Precision to Recall
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Proposed update to the documentation for WER
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I wanted to submit a minor update to the description of WER for your consideration. Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0: ``` >>> from datasets import load_metric >>> metric = load_metric("wer") >>> metric.compute(predictions=["hello how are you"], references=["hello"]) 3.0 ``` and similarly from the underlying jiwer module's `wer` function: ``` >>> from jiwer import wer >>> wer("hello", "hello how are you") 3.0 ```
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f-string formatting
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[ "Hello @lhoestq, ```make style``` is applied as asked. :)" ]
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**Fix #3257** Replaced _.format()_ and _%_ by f-strings in the following modules : - [x] **tests** - [x] **metrics** - [x] **benchmarks** - [x] **utils** - [x] **templates** - [x] **src/Datasets/\*.py** Modules in **_src/Datasets/_**: - [x] **commands** - [x] **features** - [x] **formatting** - [x] **io** - [x] **tasks** - [x] **utils** Module **datasets** will not be edited as asked by @mariosasko -A correction of the first PR (#3267)-
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Update KILT metadata JSON
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Fix #3265.
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Force data files extraction if download_mode='force_redownload'
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Avoids weird issues when redownloading a dataset due to cached data not being fully updated. With this change, issues #3122 and https://github.com/huggingface/datasets/issues/2956 (not a fix, but a workaround) can be fixed as follows: ```python dset = load_dataset(..., download_mode="force_redownload") ```
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Fix some contact information formats
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[ "The CI fail are caused by some missing sections or tags, which is unrelated to this PR. Merging !" ]
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As reported in https://github.com/huggingface/datasets/issues/3188 some contact information are not displayed correctly. This PR fixes this for CoNLL-2002 and some other datasets with the same issue
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Respect row ordering when concatenating datasets along axis=1
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Currently, there is a bug when concatenating datasets along `axis=1` if more than one dataset has the `_indices` attribute defined. In that scenario, all indices mappings except the first one get ignored. A minimal reproducible example: ```python >>> from datasets import Dataset, concatenate_datasets >>> a = Dataset.from_dict({"a": [30, 20, 10]}) >>> b = Dataset.from_dict({"b": [2, 1, 3]}) >>> d = concatenate_datasets([a.sort("a"), b.sort("b")], axis=1) >>> print(d[:3]) # expected: {'a': [10, 20, 30], 'b': [1, 2, 3]} {'a': [10, 20, 30], 'b': [3, 1, 2]} ``` I've noticed the bug while working on #3195.
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I_kwDODunzps4-y2K_
3,272
Make iter_archive work with ZIP files
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[ "Hello, is this issue open for any contributor ? can I work on it ?\r\n\r\n", "Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.\r\n\r\nTo begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:\r\n\r\nIn the `DownloadManager` for local dowloads:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242\r\n\r\nIn the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526\r\n\r\nNotice the call to `xopen` that opens and streams a file given either an URL or a local path :)", "Okay thank you for the information. I will work on this :) ", "#self-assign" ]
1,636,973,442,000
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Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive. It would be nice if it could work with ZIP files too !
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1,053,482,919
PR_kwDODunzps4uhgi1
3,271
Decode audio from remote
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Currently the Audio feature type can only decode local audio files, not remote files. To fix this I replaced `open` with our `xopen` functoin that is compatible with remote files in audio.py cc @albertvillanova @mariosasko
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1,053,465,662
PR_kwDODunzps4uhcxm
3,270
Add os.listdir for streaming
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Extend `os.listdir` to support streaming data from remote files. This is often used to navigate in remote ZIP files for example
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1,053,218,769
I_kwDODunzps4-xtfR
3,269
coqa NonMatchingChecksumError
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[ "Hi @ZhaofengWu, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.91MB/s]\r\nDownloading: 1.79kB [00:00, 1.79MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 49.0M/49.0M [00:06<00:00, 7.17MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.09M/9.09M [00:01<00:00, 6.08MB/s]\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:12<00:00, 6.48s/it]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 333.26it/s]\r\nDataset coqa downloaded and prepared to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 285.49it/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['source', 'story', 'questions', 'answers'],\r\n num_rows: 7199\r\n })\r\n validation: Dataset({\r\n features: ['source', 'story', 'questions', 'answers'],\r\n num_rows: 500\r\n })\r\n})\r\n```\r\n\r\nCould you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:\r\n```\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nIt might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists? ", "I'm getting the same error in two separate environments:\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.0\r\n```\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.5\r\n- PyArrow version: 6.0.0\r\n```", "I'm sorry, but don't get to reproduce the error in the Linux environment.\r\n\r\n@mariosasko @lhoestq can you reproduce it?", "I also can't reproduce the error on Windows/Linux (tested both the master and the `1.15.1` version). ", "Maybe the file had issues during the download ? Could you try to delete your cache and try again ?\r\nBy default the downloads cache is at `~/.cache/huggingface/datasets/downloads`\r\n\r\nAlso can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ?", "I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.\r\n```\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n```\r\nI deleted the entire `~/.cache/huggingface/datasets` on my local mac, and got a different first time error.\r\n```\r\nPython 3.9.5 (default, May 18 2021, 12:31:01) \r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.19MB/s] \r\nDownloading: 1.79kB [00:00, 712kB/s] \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.36MB/s]\r\n 50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:00<00:00, 2.47it/s]Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py\", line 1632, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 607, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 675, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/Users/zhaofengw/.cache/huggingface/modules/datasets_modules/datasets/coqa/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0/coqa.py\", line 70, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 284, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 216, in map_nested\r\n mapped = [\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 217, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True))\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 152, in _single_map_nested\r\n return function(data_struct)\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 295, in cached_path\r\n output_path = get_from_cache(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 594, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json\r\n>>> dataset = load_dataset(\"coqa\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 6.26it/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1087.45it/s]\r\n 50%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 1/2 [00:45<00:45, 45.60s/it]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py\", line 1632, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 607, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 679, in _download_and_prepare\r\n verify_checksums(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']\r\n```\r\nI can access the URL using my browser, though I did notice a redirection -- could that have something to do with it?", "Hi @ZhaofengWu, \r\n\r\nWhat about in Google Colab? Can you run this notebook without errors? \r\nhttps://colab.research.google.com/drive/1CCpiiHmtNlfO_4CZ3-fW-TSShr1M0rL4?usp=sharing", "I can run your notebook fine, but if I create one myself, it has that error: https://colab.research.google.com/drive/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.\r\n\r\nIt's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is.", "I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range...", "I mean, I don't think google colab sends the connection from my IP. Same applies to google cloud.", "Hello, I am having the same error with @ZhaofengWu first with \"social bias frames\" dataset. As I found this report, I tried also \"coqa\" and it fails as well. \r\n\r\nI test this on Google Colab. \r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\n\r\nThen another environment\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n```\r\n\r\nI tried the notebook @albertvillanova provided earlier, and it fails...\r\n", "Hi, still not able to reproduce the issue with `coqa`. If you still have this issue, could you please run these additional commands ?\r\n```python\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json\") # it returns the cached file\r\n>>> os.path.getsize(path)\r\n9090845\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n`95d427588e3733e4ebec55f6938dbba6`\r\n>>> open(path).read(500)\r\n'{\\n \"version\": \"1.0\",\\n \"data\": [\\n {\\n \"source\": \"mctest\",\\n \"id\": \"3dr23u6we5exclen4th8uq9rb42tel\",\\n \"filename\": \"mc160.test.41\",\\n \"story\": \"Once upon a time, in a barn near a farm house, there lived a little white kitten named Cotton. Cotton lived high up in a nice warm place above the barn where all of the farmer\\'s horses slept. But Cotton wasn\\'t alone in her little home above the barn, oh no. She shared her hay bed with her mommy and 5 other sisters. All of her sisters w'\r\n```\r\n\r\nThis way we can know whether you downloaded a corrupted file or an error file that could cause the `NonMatchingChecksumError` error to happen", "```\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json\") # it returns the cached file\r\n>>> os.path.getsize(path)\r\n222\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n'1195812a37c01a4481a4748c85d0c6a9'\r\n>>> open(path).read(500)\r\n'<html>\\n<head><title>503 Service Temporarily Unavailable</title></head>\\n<body bgcolor=\"white\">\\n<center><h1>503 Service Temporarily Unavailable</h1></center>\\n<hr><center>nginx/1.10.3 (Ubuntu)</center>\\n</body>\\n</html>\\n'\r\n```\r\nLooks like there was a server-side error when downloading the dataset? But I don't believe this is a transient error given (a) deleting the cache and re-downloading gives the same error; (b) it happens on multiple platforms with different network configurations; (c) other people are getting this error too, see above. So I'm not sure why it works for some people but not others.", "`wget https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json` does work. So I suspect there might be some problem in `datasets`' networking code? Can you give me some snippet that simulates how `datasets` requests the resource which I can run on my end?", "There is a redirection -- I don't know if that's the cause.", "Ok This is an issue with the server that hosts the data at `https://nlp.stanford.edu/nlp/data` that randomly returns 503 (by trying several times it also happens on my side), hopefully it can be fixed soon. I'll try to reach the people in charge of hosting the data", "Thanks. Also it might help to display a more informative error message?", "You're right. I just opened a PR that would show this error if it happens again:\r\n```python\r\nConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json (error 503)\r\n```" ]
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``` >>> from datasets import load_dataset >>> dataset = load_dataset("coqa") Downloading: 3.82kB [00:00, 1.26MB/s] Downloading: 1.79kB [00:00, 733kB/s] Using custom data configuration default Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0... Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.38MB/s] Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222/222 [00:00<00:00, 1.32MB/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.91it/s] 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1117.44it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare verify_checksums( File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json'] ```
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Dataset viewer issue for 'liweili/c4_200m'
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[ "Hi ! I think the issue comes from this [line](https://huggingface.co/datasets/liweili/c4_200m/blob/main/c4_200m.py#L87):\r\n```python\r\npath = filepath + \"/*.tsv*\"\r\n```\r\n\r\nYou can fix this by doing this instead:\r\n```python\r\npath = os.path.join(filepath, \"/*.tsv*\")\r\n```\r\n\r\nHere is why:\r\n\r\nLocally you can append `\"/*.tsv*\"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.\r\nIn streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining)\r\n\r\nTherefore in streaming mode, `filepath` is not a local path, but instead is equal to\r\n```python\r\nzip://::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip\r\n```\r\nThe `zip://` part means that we navigate inside the remote ZIP file.\r\n\r\nYou must use `os.path.join` to navigate inside it and get your TSV files:\r\n```python\r\n>>> os.path.join(filepath, \"/*.tsv*\")\r\nzip://*.tsv*::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip\r\n```\r\n\r\n`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files.", "hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you!", "Hi ! Your dataset code is all good now :)\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: d = load_dataset(\"liweili/c4_200m\", streaming=True)\r\nDownloading: 100%|█████████████████████████████████████████████| 2.79k/2.79k [00:00<00:00, 4.83MB/s]\r\nUsing custom data configuration default\r\n\r\nIn [3]: next(iter(d[\"train\"]))\r\nOut[3]: \r\n{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',\r\n 'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}\r\n```\r\nThough the viewer doesn't seem to be updated, I'll take a look at what's wrong", "thank you @lhoestq! 😄 " ]
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## Dataset viewer issue for '*liweili/c4_200m*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)* *Server Error* ``` Status code: 404 Exception: Status404Error Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist. ``` Am I the one who added this dataset ? Yes
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Replacing .format() and % by f-strings
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[ "Hi ! It looks like most of your changes are just `black` changes. All those changes are not necessary. In particular if you want to use `black`, please use the `make style` command instead. It runs `black` with additional parameters and you shouldn't end up with that many changes\r\n\r\nFeel free to open a new PR that doesn't include all the unnecessary `black` changes that you have on your branch :)", "> Hi ! It looks like most of your changes are just `black` changes. All those changes are not necessary. In particular if you want to use `black`, please use the `make style` command instead. It runs `black` with additional parameters and you shouldn't end up with that many changes\r\n> \r\n> Feel free to open a new PR that doesn't include all the unnecessary `black` changes that you have on your branch :)\r\n\r\nThank you for your answer :) , I will open a new PR with the correct changes.", "Hi @lhoestq, I submitted 3 commits in a new PR (#3277) where I did not apply black.\r\n\r\nI can apply the ```make style``` command if asked.", "Cool thanks ! Yes feel free to make sure you have `black==21.4b0` and run `make style`" ]
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**Fix #3257** Replaced _.format()_ and _%_ by f-strings in the following modules : - [x] **tests** - [x] **metrics** - [x] **benchmarks** - [x] **utils** - [x] **templates** Will follow in the next PR the modules left : - [ ] **src** Module **datasets** will not be edited as asked by @mariosasko PS : black and isort applied to files
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Fix URLs for WikiAuto Manual, jeopardy and definite_pronoun_resolution
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[ "There seems to be problems with datasets metadata, of which I dont have access to. I think one of the datasets is from reddit. Can anyone help?", "Hello @LashaO , I think the errors were caused by `_DATA_FILES` in `definite_pronoun_resolution.py`. Here are details of the test error.\r\n```\r\nself = BuilderConfig(name='plain_text', version=1.0.0, data_dir=None, data_files={'train': 'train.c.txt', 'test': 'test.c.txt'}, description='Plain text import of the Definite Pronoun Resolution Dataset.')\r\n\r\n def __post_init__(self):\r\n # The config name is used to name the cache directory.\r\n invalid_windows_characters = r\"<>:/\\|?*\"\r\n for invalid_char in invalid_windows_characters:\r\n if invalid_char in self.name:\r\n raise InvalidConfigName(\r\n f\"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. \"\r\n f\"They could create issues when creating a directory for this config on Windows filesystem.\"\r\n )\r\n if self.data_files is not None and not isinstance(self.data_files, DataFilesDict):\r\n> raise ValueError(f\"Expected a DataFilesDict in data_files but got {self.data_files}\")\r\nE ValueError: Expected a DataFilesDict in data_files but got {'train': 'train.c.txt', 'test': 'test.c.txt'}\r\n```", "Hi ! Thanks for the fixes :)\r\n\r\nInstead of uploading the `definite_pronoun_resolution` data files in this PR, maybe we can just update the URL ?\r\nThe old url was http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt, but now it's https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt (https instead of http)", "Actually the bad certificate creates an issue with the download\r\n```python\r\nimport datasets \r\ndatasets.DownloadManager().download(\"https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt\")\r\n# raises: ConnectionError: Couldn't reach https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt\r\n```\r\n\r\nLet me see if I can fix that", "I uploaded them to these URLs, feel free to use them instead of having the text files here in the PR :)\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/train.c.txt\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/test.c.txt", "Thank you for the tips! Having a busy week so anyone willing to commit the suggestions is welcome. Else, I will try to get back to this in a while.", "@LashaO Thanks for working on this. Yes, I'll take over as we already have a request to fix the URL of the Jeopardy! dataset in a separate issue.", "~~Still have to fix the error in the dummy data test of the WikiAuto dataset (so please don't merge).~~ Done! Ready for merging.", "Thank you, Mario!", "The CI failure is only related to missing tags in the dataset cards, merging :)" ]
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[#3264](https://github.com/huggingface/datasets/issues/3264)
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Checksum error for kilt_task_wow
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[ "Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.", "Hi @slyviacassell, thanks for reporting.\r\n\r\nYes, there is an issue with the checksum verification. I'm fixing it.\r\n\r\nAnd as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. " ]
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## Describe the bug Checksum failed when downloads kilt_tasks_wow. See error output for details. ## Steps to reproduce the bug ```python import datasets datasets.load_datasets('kilt_tasks','wow') ``` ## Expected results Download successful ## Actual results ``` Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a... 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s] Traceback (most recent call last): File "kilt_wow.py", line 30, in <module> main() File "kilt_wow.py", line 27, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data dataset = self.load_dataset() File "kilt_wow.py", line 21, in load_dataset return datasets.load_dataset('kilt_tasks','wow') File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare verify_checksums( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyArrow version: 4.0.1
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Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution
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[ "#take\r\nI am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.\r\n\r\nAs for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total.", "> #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.\r\n> \r\n> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.\r\n\r\nI am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot!", "No problem, buddy! Will submit a PR over this weekend." ]
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## Describe the bug - WikiAuto Manual The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author. ``` https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv ``` - jeopardy The downloading URL for jeopardy may move from ``` http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz ``` to ``` https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg ``` - definite_pronoun_resolution The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons. ``` http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt ``` ## Steps to reproduce the bug ```python import datasets datasets.load_datasets('wiki_auto','manual') datasets.load_datasets('jeopardy') datasets.load_datasets('definite_pronoun_resolution') ``` ## Expected results Download successfully ## Actual results - WikiAuto Manual ``` Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8... 0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last): File "wiki_auto.py", line 43, in <module> main() File "wiki_auto.py", line 40, in main train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data dataset = self.load_dataset() File "wiki_auto.py", line 34, in load_dataset return datasets.load_dataset('wiki_auto', 'manual') File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators data_dir = dl_manager.download_and_extract(my_urls) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested mapped = [ File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested return function(data_struct) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv ``` - jeopardy ``` Using custom data configuration default Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810... Traceback (most recent call last): File "jeopardy.py", line 45, in <module> main() File "jeopardy.py", line 42, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data dataset = self.load_dataset() File "jeopardy.py", line 36, in load_dataset return datasets.load_dataset("jeopardy") File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators filepath = dl_manager.download_and_extract(_DATA_URL) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested return function(data_struct) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz ``` - definite_pronoun_resolution ``` Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff... 0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last): File "definite_pronoun_resolution.py", line 37, in <module> main() File "definite_pronoun_resolution.py", line 34, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data dataset = self.load_dataset() File "definite_pronoun_resolution.py", line 28, in load_dataset return datasets.load_dataset('definite_pronoun_resolution') File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators files = dl_manager.download_and_extract( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested mapped = [ File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested return function(data_struct) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyArrow version: 4.0.1
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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asserts replaced with exception for image classification task, csv, json
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Fixes for csv, json in io module and image_classification task with tests referenced in https://github.com/huggingface/datasets/issues/3171
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Scifi_TV_Shows: Having trouble getting viewer to find appropriate files
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[ "Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272\r\n\r\nYou can navigate into the archive this way instead:\r\n```python\r\n# in split_generators\r\ndata_dir = dl_manager.download_and_extract(url)\r\ntrain_filepath = os.path.join(data_dir, \"all-sci-fi-data-train.txt\")\r\nreturn [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={\r\n \"filepath\": train_filepath,\r\n },\r\n ),\r\n...\r\n])\r\n\r\n# in generate_examples\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n ...\r\n```" ]
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## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus (Scifi_TV_Shows)*' **Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows) I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance! Am I the one who added this dataset? Yes
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Fix ConnectionError in Scielo dataset
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[ "The CI error is unrelated to the change." ]
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This PR: * allows 403 status code in HEAD requests to S3 buckets to fix the connection error in the Scielo dataset (instead of `url`, uses `response.url` to check the URL of the final endpoint) * makes the Scielo dataset streamable Fixes #3255.
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Updating details of IRC disentanglement data
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[ "Thank you for the cleanup!" ]
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I was pleasantly surprised to find that someone had already added my dataset to the huggingface library, but some details were missing or incorrect. This PR fixes the documentation.
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Reload dataset that was already downloaded with `load_from_disk` from cloud storage
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`load_from_disk` downloads the dataset to a temporary directory without checking if the dataset has already been downloaded once. It would be nice to have some sort of caching for datasets downloaded this way. This could leverage the fingerprint of the dataset that was saved in the `state.json` file.
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Use f-strings for string formatting
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[ "Hi, I would be glad to help with this. Is there anyone else working on it?", "Hi, I would be glad to work on this too.", "#self-assign", "Hi @Carlosbogo,\r\n\r\nwould you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?", "Oh I see. I will be glad to help with the `datasets` directory then." ]
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f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax. > **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`.
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asserts replaced by exception for text classification task with test.
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[ "Haha it looks like you got the chance of being reviewed twice at the same time and got the same suggestion twice x)\r\nAnyway it's all good now so we can merge !", "Thanks for the feedback. " ]
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I have replaced only a single assert in text_classification.py along with a unit test to verify an exception is raised based on https://github.com/huggingface/datasets/issues/3171 . I would like to first understand the code contribution workflow. So keeping the change to a single file rather than making too many changes. Once this gets approved, I will look into the rest. Thanks.
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SciELO dataset ConnectionError
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## Describe the bug I get `ConnectionError` when I am trying to load the SciELO dataset. When I try the URL with `requests` I get: ``` >>> requests.head("https://ndownloader.figstatic.com/files/14019287") <Response [302]> ``` And as far as I understand redirections in `datasets` are not supported for downloads. https://github.com/huggingface/datasets/blob/807341d0db0728073ab605c812c67f927d148f38/datasets/scielo/scielo.py#L45 ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("scielo", "en-es") ``` ## Expected results Download SciELO dataset and load Dataset object ## Actual results ``` Downloading and preparing dataset scielo/en-es (download: 21.90 MiB, generated: 68.45 MiB, post-processed: Unknown size, total: 90.35 MiB) to /Users/test/.cache/huggingface/datasets/scielo/en-es/1.0.0/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e... Traceback (most recent call last): File "scielo.py", line 3, in <module> dataset = load_dataset("scielo", "en-es") File "../lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "../lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "../lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/test/.cache/huggingface/modules/datasets_modules/datasets/scielo/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e/scielo.py", line 77, in _split_generators data_dir = dl_manager.download_and_extract(_URLS[self.config.name]) File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "../lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested return function(data_struct) File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://ndownloader.figstatic.com/files/14019287 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.12 - PyArrow version: 6.0.0
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Update xcopa dataset (fix checksum issues + add translated data)
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[ "The CI failures are unrelated to the changes (missing fields in the readme and the CER metric error fixed in #3252)." ]
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This PR updates the checksums (as reported [here](https://discuss.huggingface.co/t/how-to-load-dataset-locally/11601/2)) of the `xcopa` dataset. Additionally, it adds new configs that hold the translated data of the original set of configs. This data was not available at the time of adding this dataset to the lib.
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`GeneratorBasedBuilder` does not support `None` values
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[ "Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835\r\n\r\nI expect that PR to be merged soon." ]
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## Describe the bug `GeneratorBasedBuilder` does not support `None` values. ## Steps to reproduce the bug See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction. ## Expected results Dataset is initialized with a `None` value in the `value` column. ## Actual results ``` Traceback (most recent call last): File "main.py", line 3, in <module> datasets.load_dataset("./bad-data") File ".../datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File ".../datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File ".../datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".../datasets/builder.py", line 1103, in _prepare_split example = self.info.features.encode_example(record) File ".../datasets/features/features.py", line 1033, in encode_example return encode_nested_example(self, example) File ".../datasets/features/features.py", line 808, in encode_nested_example return { File ".../datasets/features/features.py", line 809, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File ".../datasets/features/features.py", line 855, in encode_nested_example return schema.encode_example(obj) File ".../datasets/features/features.py", line 299, in encode_example return float(value) TypeError: float() argument must be a string or a number, not 'NoneType' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 6.0.0
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Fix failing CER metric test in CI after update
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Fixes the [failing CER metric test](https://app.circleci.com/pipelines/github/huggingface/datasets/8644/workflows/79816553-fa2f-4756-b022-d5937f00bf7b/jobs/53298) in CI by adding support for `jiwer==2.3.0`, which was released yesterday. Also, I verified that all the tests in `metrics/cer/test_cer.py` pass after the change, so the results should be the same irrespective of the `jiwer` version.
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Add ETHICS dataset
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This PR adds the ETHICS dataset, including all 5 sub-datasets. From https://arxiv.org/abs/2008.02275
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Fix streaming for id_newspapers_2018
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To be compatible with streaming, this dataset must use `dl_manager.iter_archive` since the data are in a .tgz file
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Stream from Google Drive and other hosts
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[ "I just tried some datasets and noticed that `spider` is not working for some reason (the compression type is not recognized), resulting in FileNotFoundError. I can take a look tomorrow", "I'm fixing the remaining files based on TAR archives", "THANKS A LOT" ]
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Streaming from Google Drive is a bit more challenging than the other host we've been supporting: - the download URL must be updated to add the confirm token obtained by HEAD request - it requires to use cookies to keep the connection alive - the URL doesn't tell any information about whether the file is compressed or not Therefore I did two things: - I added a step for URL and headers/cookies preparation in the StreamingDownloadManager - I added automatic compression type inference by reading the [magic number](https://en.wikipedia.org/wiki/List_of_file_signatures) This allows to do do fancy things like ```python from datasets.utils.streaming_download_manager import StreamingDownloadManager, xopen, xjoin, xglob # zip file containing a train.tsv file url = "https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh" extracted = StreamingDownloadManager().download_and_extract(url) for inner_file in xglob(xjoin(extracted, "*.tsv")): with xopen(inner_file) as f: # streaming starts here for line in f: print(line) ``` This should make around 80 datasets streamable. It concerns those hosted on Google Drive but also any dataset for which the URL doesn't give any information about compression. Here is the full list: ``` amazon_polarity, ami, arabic_billion_words, ascent_kb, asset, big_patent, billsum, capes, cmrc2018, cnn_dailymail, code_x_glue_cc_code_completion_token, code_x_glue_cc_code_refinement, code_x_glue_cc_code_to_code_trans, code_x_glue_tt_text_to_text, conll2002, craigslist_bargains, dbpedia_14, docred, ehealth_kd, emo, euronews, germeval_14, gigaword, grail_qa, great_code, has_part, head_qa, health_fact, hope_edi, id_newspapers_2018, igbo_english_machine_translation, irc_disentangle, jfleg, jnlpba, journalists_questions, kor_ner, linnaeus, med_hop, mrqa, mt_eng_vietnamese, multi_news, norwegian_ner, offcombr, offenseval_dravidian, para_pat, peoples_daily_ner, pn_summary, poleval2019_mt, pubmed_qa, qangaroo, reddit_tifu, refresd, ro_sts_parallel, russian_super_glue, samsum, sberquad, scielo, search_qa, species_800, spider, squad_adversarial, tamilmixsentiment, tashkeela, ted_talks_iwslt, trec, turk, turkish_ner, twi_text_c3, universal_morphologies, web_of_science, weibo_ner, wiki_bio, wiki_hop, wiki_lingua, wiki_summary, wili_2018, wisesight1000, wnut_17, yahoo_answers_topics, yelp_review_full, yoruba_text_c3 ``` Some of them may not work if the host doesn't support HTTP range requests for example Fix https://github.com/huggingface/datasets/issues/2742 Fix https://github.com/huggingface/datasets/issues/3188
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Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError
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[ "Hi,\r\n\r\nthis issue is similar to https://github.com/huggingface/datasets/issues/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).\r\n\r\n@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our side.", "I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?\r\nAlthough maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray" ]
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## Describe the bug When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct` Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work. Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works ## Steps to reproduce the bug ```python load_dataset("json", data_files="test.json") ``` test.json ~25MB ```json {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} ... ``` working.json ~160bytes ```json {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} ``` ## Expected results It should load the dataset from the json file without error. ## Actual results It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct` ``` Traceback (most recent call last): File "/Users/m/workspace/xxx/project/main.py", line 60, in <module> dataset = load_dataset("json", data_files="result.json") File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset builder_instance.download_and_prepare( File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct ``` ## Environment info - `datasets` version: 1.14.0 - Platform: macOS-12.0.1-arm64-arm-64bit - Python version: 3.9.7 - PyArrow version: 6.0.0
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[tiny] fix typo in stream docs
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Fix load_from_disk temporary directory
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`load_from_disk` uses `tempfile.TemporaryDirectory()` instead of our `get_temporary_cache_files_directory()` function. This can cause the temporary directory to be deleted before the dataset object is garbage collected. In practice, it prevents anyone from using methods like `shuffle` on a dataset loaded this way, because it can't write the shuffled indices in a directory that doesn't exist anymore. In this PR I switch to using `get_temporary_cache_files_directory()` and I update the tests. cc @mariosasko since you worked on `get_temporary_cache_files_directory()`
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Fix filter method for batched=True
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Remove redundant isort module placement
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`isort` can place modules by itself from [version 5.0.0](https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0.html#module-placement-changes-known_third_party-known_first_party-default_section-etc) onwards, making the `known_first_party` and `known_third_party` fields in `setup.cfg` redundant (this is why our CI works, even though we haven't touched these options in a while).
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Adding ANERcorp-CAMeLLab dataset
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[ "Adding ANERcorp dataset\r\n\r\n## Adding a Dataset\r\n- **Name:** *ANERcorp-CAMeLLab*\r\n- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.\r\n\r\nIn 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*\r\n\r\n- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. \"Anersys: An Arabic named entity recognition system based on maximum entropy.\" In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.\r\n\r\n(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. \"CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing.\" In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*\r\n- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*\r\n- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)." ]
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Swap descriptions of v1 and raw-v1 configs of WikiText dataset and fix metadata
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Fix #3237.
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Couldn't reach data file for disaster_response_messages
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[ "It looks like the dataset isn't available anymore on appen.com\r\n\r\nThe CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?" ]
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## Describe the bug Following command gives an ConnectionError. ## Steps to reproduce the bug ```python disaster = load_dataset('disaster_response_messages') ``` ## Error ``` ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv ``` ## Expected results It should load dataset without an error ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Google Colab - Python version: 3.7 - PyArrow version:
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Inconsistent performance of the "arabic_billion_words" dataset
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## Describe the bug When downloaded from macine 1 the dataset is downloaded and parsed correctly. When downloaded from machine two (which has a different cache directory), the following script: import datasets from datasets import load_dataset raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload') gives the following error: **Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17... Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348M/348M [00:24<00:00, 14.0MB/s] Traceback (most recent call last): File ".../why_mismatch.py", line 3, in <module> File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 709, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words')}]** Note that the package versions of datasets (1.15.1) and rarfile (4.0) are identical. ## Steps to reproduce the bug import datasets from datasets import load_dataset raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload') # Sample code to reproduce the bug ## Expected results Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17... Downloading: 100%|███████████████████████████| 348M/348M [00:22<00:00, 15.8MB/s] Dataset arabic_billion_words downloaded and prepared to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17. Subsequent calls will reuse this data. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> Machine 1: - `datasets` version: 1.15.1 - Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 4.0.1 Machine 2 (the bugged one) - `datasets` version: 1.15.1 - Platform: Linux-4.4.0-210-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 6.0.0
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Reuters21578 Couldn't reach
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[ "Hi ! The URL works fine on my side today, could you try again ?", "thank you @lhoestq \r\nit works" ]
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``## Adding a Dataset - **Name:** *Reuters21578* - **Description:** *ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz* - **Data:** *https://huggingface.co/datasets/reuters21578* `from datasets import load_dataset` `dataset = load_dataset("reuters21578", 'ModLewis')` ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz And I try to request the link as follow: `import requests` `requests.head('https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz')` SSLError: HTTPSConnectionPool(host='kdd.ics.uci.edu', port=443): Max retries exceeded with url: /databases/reuters21578/reuters21578.tar.gz (Caused by SSLError(SSLError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)'),)) This problem likes #575 What should I do ?
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wikitext description wrong
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[ "Hi @hongyuanmei, thanks for reporting.\r\n\r\nI'm fixing it." ]
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## Describe the bug Descriptions of the wikitext datasests are wrong. ## Steps to reproduce the bug Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50 ## Expected results The descriptions for raw-v1 and v1 should be switched.
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Loading of datasets changed in #3110 returns no examples
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[ "Hi @eladsegal, thanks for reporting.\r\n\r\nI am sorry, but I can't reproduce the bug:\r\n```\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"qasper\")\r\nDownloading: 5.11kB [00:00, ?B/s]\r\nDownloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...\r\nDataset qasper downloaded and prepared to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<?, ?it/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n num_rows: 888\r\n })\r\n validation: Dataset({\r\n features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n num_rows: 281\r\n })\r\n})\r\n``` \r\n\r\nThis makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.\r\n\r\n@lhoestq might it be possible that the code change introduced by #3110 makes \"inaccessible\" all previously cached TAR-based datasets?\r\n- Before the caching system downloaded and extracted the tar dataset\r\n- Now it only downloads the tar dataset (no extraction is done)", "I can't reproduce either in my environment (macos, python 3.7).\r\n\r\nIn your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.\r\n\r\nTherefore here is my hypothesis:\r\n- either the cache is corrupted somehow with an empty TAR archive\r\n- OR I suspect that the issue comes from python 3.8\r\n", "I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix", "Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110\r\n\r\nTo fix this you just have to pull the latest changes from `master` :)\r\n\r\nLet me know if that helps !\r\n\r\n--------------\r\n\r\nHere are more details about my investigation:\r\n\r\nIt's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).\r\n\r\nIn particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.\r\n\r\nTherefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this.", "Thanks!\r\nBut what about code that is already using older version of datasets? \r\nThe reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.\r\nI handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this.", "Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.\r\n\r\nIt only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).\r\n", "OK, I understand the issue a bit better now.\r\nI see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.\r\nSo users that use an old dev version must specify revision or else they'll encounter this problem.\r\n\r\nBTW, when I opened the issue I installed the latest master version with\r\n```\r\npip install git+git://github.com/huggingface/datasets@master#egg=datasets\r\n```\r\nand also used `download_mode=\"force_redownload\"`, and it still returned 0 examples.\r\nNow I deleted all of the cache and ran the code again, and it worked.\r\nI'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.\r\n\r\nThanks again!" ]
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## Describe the bug Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 0 }) validation: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 0 }) }) ``` ## Steps to reproduce the bug Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110: ```python from datasets import load_dataset load_dataset("qasper") # The problem only started with the commit of #3110 load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780") ``` ## Expected results ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 888 }) validation: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 281 }) }) ``` Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110: ```python from datasets import load_dataset load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d") ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.2.dev0 (master) - Python version: 3.8.10 - PyArrow version: 3.0.0
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Addd options to use updated bleurt checkpoints
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Adds options to use newer recommended checkpoint (as of 2021/10/8) bleurt-20 and its distilled versions. Updated checkpoints are described in https://github.com/google-research/bleurt/blob/master/checkpoints.md#the-recommended-checkpoint-bleurt-20 This change won't affect the default behavior of metrics/bleurt. It only adds option to load newer checkpoints as `datasets.load_metric('bleurt', 'bleurt-20')` `bluert-20` generates scores roughly between 0 and 1, which wasn't the case for the previous checkpoints.
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Avoid PyArrow type optimization if it fails
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[ "That's good to have a way to disable this easily :)\r\nI just find it a bit unfortunate that users would have to experience the error once and then do `DISABLE_PYARROW_TYPES_OPTIMIZATION=1`. Do you know if there's a way to simply fallback on disabling it automatically when it fails ?", "@lhoestq Actually, I agree a fallback makes more sense. The current approach is not very practical indeed and would require a mention in the docs.\r\n", "Replaced the env variable with a fallback!", "Hmm if the fallback automatically happens without the user knowing it, then I don't think we really need to mention it. But if you really wanted to, I think the [Improve performance](https://huggingface.co/docs/datasets/cache.html#improve-performance) section would be a great place for it! ", "Yea I think this could just end up in a note that says that `datasets` automatically picks the most optimized integer precision for your tokenized text data to save you disk space. Maybe later if we have a page on text processing we can add this note, but for now I agree it doesn't fit well into the doc.\r\n\r\nIn particular in the \"Improve performance\" section we mention what users can do to speed up their computations, while this behavior is just some internal feature that users don't have control over anyway." ]
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Adds a new variable, `DISABLE_PYARROW_TYPES_OPTIMIZATION`, to `config.py` for easier control of the Arrow type optimization. Fix #2206
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Improve repository structure docs
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Continuation of the documentation started in https://github.com/huggingface/datasets/pull/3221, taking into account @stevhliu 's comments
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The Xsum datasets seems not able to download.
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[ "Hi ! On my side the URL is working fine, could you try again ?", "> Hi ! On my side the URL is working fine, could you try again ?\r\n\r\nI try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)? :>", "I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.", "> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.\r\n\r\n:> ok. Thanks for your reply." ]
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## Describe the bug The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download. ## Steps to reproduce the bug ```python load_dataset('xsum') ``` ## Actual results ``` python raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz ```
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Group tests in multiprocessing workers by test file
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By grouping tests by test file, we make sure that all the tests in `test_load.py` are sent to the same worker. Therefore, the fixture `hf_token` will be called only once (and from the same worker). Related to: #3200. Fix #3219.
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Add full tagset to conll2003 README
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[ "I also added the missing `pretty_name` tag in the dataset card to fix the CI" ]
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Even though it is possible to manually get the tagset list with ```python dset.features[field_name].feature.names ``` I think it is useful to have an overview of the used tagset on the dataset card. This is particularly useful in light of the **dataset viewer**: the tags are encoded, so it is not immediately obvious what they are for a given sample. Adding a label-int mapping should make it easier for visitors to get a grasp of what they mean. From user-experience perspective, I would urge the full tagsets to always be available in the README's but I understand that that would take a lot of work, probably. Perhaps it can be automated? closes #3189
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Fix URL in CITATION file
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Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL): ``` @inproceedings{Lhoest_Datasets_A_Community_2021, author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaško, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, month = {11}, pages = {175--184}, publisher = {Association for Computational Linguistics}, title = {{Datasets: A Community Library for Natural Language Processing}}, url = {https://github.com/huggingface/datasets}, year = {2021} } ```
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Add CITATION file
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Add CITATION file.
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Error in `Json(datasets.ArrowBasedBuilder)` class
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[ "I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file\r\n`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not being entered correctly:\r\n```python\r\n if (\r\n isinstance(e, pa.ArrowInvalid)\r\n and \"straddling\" not in str(e)\r\n or block_size > len(batch)\r\n ):\r\n```\r\n\r\nFrom what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain \"straddling\" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L135).\r\n\r\nChanging the condition above to simply\r\n```python\r\n if (\r\n block_size > len(batch)\r\n ):\r\n```\r\n\r\nFixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary.", "Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.\r\nEach example should be on one single line.\r\n\r\nCan you try fixing the format to have one line per example and try again ?", ":open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much!" ]
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## Describe the bug When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails. ## Steps to reproduce the bug Create a folder that contains the following: ``` . ├── testdata │   └── mydata.json └── test.py ``` Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide) :exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`! `test.py` simply contains: ```python from datasets import load_dataset my_dataset = load_dataset("testdata") ``` To reproduce the error, simply run ``` python test.py ``` ## Expected results The data should load correctly without error. ## Actual results The dataset builder fails with: ``` Using custom data configuration testdata-d490389b8ab4fd82 Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34... 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s] Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0 Traceback (most recent call last): File "test.py", line 28, in <module> my_dataset = load_dataset("testdata") File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split for key, table in utils.tqdm( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__ for obj in iterable: File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise ValueError( ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.0
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Fix paper BibTeX citation with proceedings reference
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Fix paper BibTeX citation with proceedings reference.
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Update tatoeba to v2021-07-22
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[ "How about this? @lhoestq @abhishekkrthakur ", "Hi ! I think it would be nice if people could still be able to load the old version.\r\nMaybe this can be a parameter ? For example to load the old version they could do\r\n```python\r\nload_dataset(\"tatoeba\", lang1=\"en\", lang2=\"mr\", date=\"v2020-11-09\")\r\n```\r\n\r\nIf it sounds good to you, we can add this parameter to the TatoebaConfig:\r\n```python\r\nclass TatoebaConfig(datasets.BuilderConfig):\r\n def __init__(self, *args, lang1=None, lang2=None, date=\"v2021-07-22\", **kwargs):\r\n self.date = date\r\n```\r\nand then pass the date to the URL\r\n```python\r\n_BASE_URL = \"https://object.pouta.csc.fi/OPUS-Tatoeba/{}/moses/{}-{}.txt.zip\"\r\n```\r\n```python\r\n def _base_url(lang1, lang2, date):\r\n return _BASE_URL.format(date, lang1, lang2)\r\n```\r\n\r\nWhat do you think ?", "`_DATE = \"v\" + \"-\".join(s.zfill(2) for s in _VERSION.split(\".\"))` seems rather tricky but works well. How about this? @lhoestq \r\n", "The CI is only failing because of the missing sections in the dataset card, and because of an issue with the CER metric that is unrelated to this PR" ]
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Tatoeba's latest version is v2021-07-22
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User-pickling with dynamic sub-classing
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[ "@lhoestq Feel free to have a look. The implementation is slightly different from what you suggested. I have opted to overwrite `save` instead of meddling with `save_global`. `save_global` is called very late down in dill/pickle so it is hard to control for what is happening there. I might be wrong. Pickling is more complex than I thought! \r\n\r\nThe linked issue (`map` with spaCy) also works now!\r\n\r\n```python\r\nimport pickle\r\nimport spacy\r\nfrom spacy import Language\r\nfrom datasets import load_dataset\r\nfrom datasets.utils.py_utils import dumps, pklregister\r\n\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, nlp: Language):\r\n pickler.save(nlp.to_bytes())\r\n\r\ndef main():\r\n fin = r\"large/file.txt\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n\r\n def tokenize(l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n ds = load_dataset(\"text\", data_files=fin)\r\n ds = ds[\"train\"].map(tokenize)\r\n\r\n # Sanity check: load NLP from pickle created with our own `dumps`\r\n config = nlp.config\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp2 = lang_cls.from_config(config)\r\n nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n\r\n assert isinstance(nlp2, type(nlp))\r\n assert dumps(nlp) == dumps(nlp2)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\nIf this all looks good to you, I'll start writing on some documentation and examples.\r\n", "One more thing. This is a reduction function for SpaCy Language that should work with the new API:\r\n```python\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, obj):\r\n def create_language(config, bytes_data):\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp = lang_cls.from_config(config)\r\n return nlp.from_bytes(bytes_data)\r\n\r\n args = (obj.config, obj.to_bytes())\r\n pickler.save_reduce(create_language, args, obj=obj)\r\n```\r\nso IMO we are missing a test with `pickler.save_reduce`. ", "> One more thing. This is a reduction function for SpaCy Language that should work with the new API:\r\n> \r\n> ```python\r\n> @pklregister(Language, allow_subclasses=True)\r\n> def hash_spacy_language(pickler, obj):\r\n> def create_language(config, bytes_data):\r\n> lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n> nlp = lang_cls.from_config(config)\r\n> return nlp.from_bytes(bytes_data)\r\n> \r\n> args = (obj.config, obj.to_bytes())\r\n> pickler.save_reduce(create_language, args, obj=obj)\r\n> ```\r\n> \r\n> so IMO we are missing a test with `pickler.save_reduce`.\r\n\r\nSure that seems a good idea, but I do not quite understand what `save_reduce` does. Could you give some more info about what reduce functions do and how they differ from regular `save` and `save_global`? I've read about it but the docs nor the built-in `pickle` code seem really helpful.", "I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.\r\n\r\nFor example your sanity check could be simplified from\r\n```python\r\n config = nlp.config\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp2 = lang_cls.from_config(config)\r\n nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n```\r\nto\r\nEDIT: <s>pickle.loads(pickle.dumps(nlp))</s>\r\n```python\r\n nlp2 = loads(dumps(nlp)) # using our custom pickler\r\n```\r\n\r\nThough note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.", "> I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.\r\n> \r\n> For example your sanity check could be simplified from\r\n> \r\n> ```python\r\n> config = nlp.config\r\n> lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n> nlp2 = lang_cls.from_config(config)\r\n> nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n> ```\r\n> \r\n> to\r\n> \r\n> ```python\r\n> nlp2 = pickle.loads(pickle.dumps(nlp))\r\n> ```\r\n> \r\n> Though note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.\r\n\r\nYes, the sanity check can be simplified like that _if_ we use `pickle.dumps` - but that would not test our own `dumps` functionality and would do a naive dump instead of using `to_bytes`. It won't work if we use our own `dumps`, exactly because of the reason that we want custom pickling and being able to call `to_bytes`. To reconstruct the object from the pickled bytes from `to_bytes` we need `from_bytes`. The result of pickle/dill loads will therefore always be a `bytes` object and not a `Language` object.\r\n\r\nBut `save_reduce` is called when saving, right? Not when loading, AFAICT. I am just not sure what exactly it is saving. It is _potentially_ called [at the end of `save`](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L603) but only if we haven't returned by then. I just can't figure out what that base case is.", "I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with `save` ?", "@BramVanroy \r\nAs I understand `save_reduce` is very similar to `copyreg.pickle`, so I'd suggest you to check the following links:\r\n* https://docs.python.org/3/library/copyreg.html#copyreg.pickle\r\n* https://docs.python.org/3/library/pickle.html#object.__reduce__\r\n\r\n\r\n@lhoestq \r\n> I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with save ?\r\n\r\nI agree. \r\n\r\n`save_reduce` is very similar to `copyreg.pickle` and `object.__reduce__`, which are part of public API (and `save` isn't), so I expect more advanced users to know how to write their own reduction functions. But, as you say, `pklregister` should also work with `save` (even though I think `save` is a bit lower-level, and harder to understand than `save_reduce`).\r\n\r\nAll our examples in `py_utils` that use `pklregister` also use `save_reduce` in the last step, so my reduction for SpaCy is meant to be added there, and not to be written by users (because SpaCy is very popular, so the official support by us makes sense :)).\r\n\r\nAnd in the tests, let's ignore the reconstruction part of pickle/dill, because it's not important for us, and focus on the generated dumps. What do you think?", "@mariosasko What exactly do you mean with \"isn't part of the public API\"? It is [a public method](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L535) in base pickle, just like `dump` is but maybe you mean something else.", "@BramVanroy Oh sorry, it's public (not prefixed with `\"_\"`) but it's not documented in the docs. `save_reduce` is also not in the docs, but its signature/functionality is similar to `copyreg.pickle` and I see it more often being used in the projects on GH, so it's seems \"more public\" to me. ", "Unfortunately I feel that pickle in general is under-documented. 😄 \r\n\r\nFor the documentation, I can add a brief example, maybe under \"How-to Guides\"? The only thing that isn't immediately obvious to me is how I can add that doc page to the TOC?", "Yes great idea ! To add that doc page to the TOC, you just have to add it to the index.rst file in the \"How-to guides\" TOC section", "@mariosasko @lhoestq Feel free to make any edits or suggestions in the text!", "Hi @mariosasko. I wish you'd told me sooner, as I spent quite some time writing on this.\r\n\r\nI'm also not sure whether it is too advanced to have in the documentation. The spaCy use-case seems potentially frequent. Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?", "Documenting the internal `pklregister` is overkill IMO (and it can be kept in docstrings), but we can document something higher level like `register_hash_func` once it's implemented.\r\n\r\nSo we keep the nice documentation you've written (thank you!), except we can rename it to \"Advanced caching\" and show an API that is similar to\r\n```python\r\n>>> @register_hash_func(Language, allow_subclasses=True)\r\n>>> def hash_spacy_language(nlp: Language):\r\n>>> return (nlp.to_bytes(),)\r\n```\r\nThis way we keep the documentation centered around the public API rather than the internals that may evolve/be too complicated to fit only one section.\r\n\r\n> Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?\r\n\r\nLet's add it to the defaults since it's a frequent use-case. And also allow users to control the hashing using the API mentioned above if they face other non-trivially-hashable objects", "Sure, I can have a go at implementing spaCy as a built-in. Should it be included in the tests? (Therefore adding spaCy to the tests requirements.)\r\n\r\nNext, from your example, it seems that the return value of `register_hash_func` will be used in pickler.save automatically (calling pklregister a bit deeper). Any reason why it returns a tuple? I can work on this as well, if needed.", "> Sure, I can have a go at implementing spaCy as a built-in. Should it be included in the tests? (Therefore adding spaCy to the tests requirements.)\r\n\r\nThat would be perfect !\r\n\r\n> Next, from your example, it seems that the return value of register_hash_func will be used in pickler.save automatically (calling pklregister a bit deeper). \r\n\r\nYes I think so. For example register_hash_func can call pklregister with the user's function, but wrapped to use pickler.save.\r\n\r\n> Any reason why it returns a tuple? I can work on this as well, if needed.\r\n\r\nIt can either return an arbitrary object or a tuple. I like it a bit better if it's a tuple, so users understand more easily how to make the function take into account more than one item for the hash. It's also consistent with the streamlit caching functions, that also require a tuple. No strong opinion on this though\r\n\r\nLet me know if I can help with anything" ]
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This is a continuation of the now closed PR in https://github.com/huggingface/datasets/pull/3206. The discussion there has shaped a new approach to do this. In this PR, behavior of `pklregister` and `Pickler` is extended. Earlier, users were already able to register custom pickle functions. That is useful if they have objects that are not easily picklable with default methods. When one registers a custom function to a type, an object of that type will be pickled with the given function by `Pickler` which looks up the type in its `dispatch` table. The downside of this method, and of `pickle` in general, is that it is limited to direct type-matching and does not allow sub-classes. In many, default, cases that is not an issue. But when you are using external libraries where classes (e.g. parsers, models) are sub-classed this is not ideal. ```python from datasets.fingerprint import Hasher from datasets.utils.py_utils import pklregister class BaseParser: pass class EnglishParser(BaseParser): pass @pklregister(BaseParser) def custom_pkl_func(pickler, obj): print(f"Called the custom pickle function for type {type(obj)}!") # do something with the obj and ultimately save with the pickler base = BaseParser() en = EnglishParser() # Hasher.hash uses the Pickler behind the scenes # `custom_pkl_func` called for base Hasher.hash(base) # `custom_pkl_func` not called for en :-( Hasher.hash(en) ``` In the example above we'd want to sub-class `EnglishParser` to be handled in the same way as its super-class `BaseParser`. This PR solves that by allowing for a keyword-argument `allow_subclasses` in `pklregister` (default: `False`). ```python @pklregister(BaseParser, allow_subclasses=True) ``` When this option is enabled, we not only save the function in `Pickler.dispatch` but also save it in a custom table `Pickler.subclass_dispatch` **which allows us to dynamically add sub-classes of that class to the real dispatch table**. Then, if we want to pickle an object `obj` with `Pickler.dump()` (which ultimately will call `Pickler.save()`) we _first_ check whether any of the object's super-classes exist in `Pickler.sublcass_dispatch` and get the related custom pickle function. If we find one, we add the type of `obj` alongside the function to `Pickler.dispatch`. All of this happens at the start of the call to `Pickler.save()`. _Only then_ dill.Pickler's `save` will be called, which in turn will call `pickle._Pickler.save` which handles everything. Here, the `Pickler.dispatch` table will be used to look up custom pickler functions - and it now also includes the function for `obj`, which was copied from its super-class, which we added at the very start of our custom `Pickler.save()`. For edge cases and, especially, for testing, a contextmanager class `TempPickleRegistry` is included that resets the pickle registry on exit to its previous state. ```python with TempPickleRegistry(): @pklregister(MyObjClass) def pickle_registry_test_false(pickler, obj): pickler.save(obj.fancy_method()) some_obj = MyObjClass() dumps(some_obj) # `MyObjClass` is in Pickler.dispatch # ... `MyObjClass` is _not_ in Pickler.dispatch anymore ``` closes https://github.com/huggingface/datasets/issues/3178 To Do ==== - [x] Write tests - [ ] Write documentation/examples?
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Update BibTeX entry
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Update BibTeX entry.
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Add docs for audio processing
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[ "Nice ! love it this way. I guess you can set this PR to \"ready for review\" ?", "I guess we can merge this one now :)" ]
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This PR adds documentation for the `Audio` feature. It describes: - The difference between loading `path` and `audio`, as well as use-cases/best practices for each of them. - Resampling audio files with `cast_column`, and then calling `ds[0]["audio"]` to automatically decode and resample to the desired sampling rate. - Resampling with `map`. Preview [here](https://52969-250213286-gh.circle-artifacts.com/0/docs/_build/html/audio_process.html), let me know if I'm missing anything!
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Resolve data_files by split name
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[ "Really cool!\r\nWhen splitting by folder, what do we use for validation set (\"valid\", \"validation\" or both)?", "> When splitting by folder, what do we use for validation set (\"valid\", \"validation\" or both)?\r\n\r\nBoth are fine :) As soon as it has \"valid\" in it", "Merging for now, if you have comments about the documentation we can address them in subsequent PRs :)", "Thanks for the comments @stevhliu :) I just opened https://github.com/huggingface/datasets/pull/3233 to take them into account" ]
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As discussed in https://github.com/huggingface/datasets/issues/3027 we should automatically infer what file is supposed to go to what split automatically, based on filenames. I added the support for different kinds of patterns, for both dataset repositories and local directories: ``` Input structure: my_dataset_repository/ ├── README.md └── dataset.csv Output patterns: {"train": ["*"]} ``` ``` Input structure: my_dataset_repository/ ├── README.md ├── train.csv └── test.csv my_dataset_repository/ ├── README.md └── data/ ├── train.csv └── test.csv my_dataset_repository/ ├── README.md ├── train_0.csv ├── train_1.csv ├── train_2.csv ├── train_3.csv ├── test_0.csv └── test_1.csv Output patterns: {"train": ["*train*"], "test": ["*test*"]} ``` ``` Input structure: my_dataset_repository/ ├── README.md └── data/ ├── train/ │ ├── shard_0.csv │ ├── shard_1.csv │ ├── shard_2.csv │ └── shard_3.csv └── test/ ├── shard_0.csv └── shard_1.csv Output patterns: {"train": ["*train*/*", "*train*/**/*"], "test": ["*test*/*", "*test*/**/*"]} ``` and also this pattern that allows to have custom split names, and that is the structure used by #3098 for `push_to_hub` (cc @LysandreJik ): ``` Input structure: my_dataset_repository/ ├── README.md └── data/ ├── train-00000-of-00003.csv ├── train-00001-of-00003.csv ├── train-00002-of-00003.csv ├── test-00000-of-00001.csv ├── random-00000-of-00003.csv ├── random-00001-of-00003.csv └── random-00002-of-00003.csv Output patterns: { "train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], } ``` You can check the documentation about structuring your repository [here](https://52640-250213286-gh.circle-artifacts.com/0/docs/_build/html/repository_structure.html). cc @stevhliu Fix https://github.com/huggingface/datasets/issues/3027 Fix https://github.com/huggingface/datasets/issues/3212 In the future we can also add support for dataset configurations.
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Add documentation about dataset viewer feature
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Add to the docs more details about the dataset viewer feature in the Hub. CC: @julien-c
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Eventual Invalid Token Error at setup of private datasets
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## Describe the bug From time to time, there appear Invalid Token errors with private datasets: - https://app.circleci.com/pipelines/github/huggingface/datasets/8520/workflows/d44629f2-4749-40f8-a657-50931d0b3434/jobs/52534 ``` ____________ ERROR at setup of test_load_streaming_private_dataset _____________ ValueError: Invalid token passed! ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____ ValueError: Invalid token passed! =========================== short test summary info ============================ ERROR tests/test_load.py::test_load_streaming_private_dataset - ValueError: I... ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data ``` - https://app.circleci.com/pipelines/github/huggingface/datasets/8557/workflows/a8383181-ba6d-4487-9d0a-f750b6dcb936/jobs/52763 ``` ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____ [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 hf_api = <huggingface_hub.hf_api.HfApi object at 0x7f4899bab908> hf_token = 'vgNbyuaLNEBuGbgCEtSBCOcPjZnngJufHkTaZvHwkXKGkHpjBPwmLQuJVXRxBuaRzNlGjlMpYRPbthfHPFWXaaEDTLiqTTecYENxukRYVAAdpeApIUPxcgsowadkTkPj' zip_csv_path = PosixPath('/tmp/pytest-of-circleci/pytest-0/popen-gw1/data16/dataset.csv.zip') @pytest.fixture(scope="session") def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_path): repo_name = "repo_zipped_txt_data-{}".format(int(time.time() * 10e3)) hf_api.create_repo(token=hf_token, name=repo_name, repo_type="dataset", private=True) repo_id = f"{USER}/{repo_name}" hf_api.upload_file( token=hf_token, path_or_fileobj=str(zip_csv_path), path_in_repo="data.zip", repo_id=repo_id, > repo_type="dataset", ) tests/hub_fixtures.py:68: ... ValueError: Invalid token passed! =========================== short test summary info ============================ ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data ```
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Fix code quality in riddle_sense dataset
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Fix trailing whitespace. Fix #3217.
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Fix code quality bug in riddle_sense dataset
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[ "To give more context: https://github.com/psf/black/issues/318. `black` doesn't treat this as a bug, but `flake8` does. \r\n" ]
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## Describe the bug ``` datasets/riddle_sense/riddle_sense.py:36:21: W291 trailing whitespace ```
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Pin version exclusion for tensorflow incompatible with keras
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Once `tensorflow` version 2.6.2 is released: - https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb - https://pypi.org/project/tensorflow/2.6.2/ with the patch: - tensorflow/tensorflow#52927 we can remove the temporary fix we introduced in: - #3208 Fix #3209.
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Small updates to to_tf_dataset documentation
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[ "@stevhliu Accepted both suggestions, thanks for the review!" ]
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I added a little more description about `to_tf_dataset` compared to just setting the format
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Add ACAV100M Dataset
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## Adding a Dataset - **Name:** *ACAV100M* - **Description:** *contains 100 million videos with high audio-visual correspondence, ideal for self-supervised video representation learning.* - **Paper:** *https://arxiv.org/abs/2101.10803* - **Data:** *https://github.com/sangho-vision/acav100m* - **Motivation:** *The largest dataset (to date) for audio-visual learning.* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Fix tuple_ie download url
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Fix #3204
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Sort files before loading
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[ "This will be fixed by https://github.com/huggingface/datasets/pull/3221" ]
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When loading a dataset that consists of several files (e.g. `my_data/data_001.json`, `my_data/data_002.json` etc.) they are not loaded in order when using `load_dataset("my_data")`. This could lead to counter-intuitive results if, for example, the data files are sorted by date or similar since they would appear in different order in the `Dataset`. The straightforward solution is to sort the list of files alphabetically before loading them. cc @lhoestq
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Fix disable_nullable default value to False
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Currently the `disable_nullable` parameter is not consistent across all dataset transforms. For example it is `False` in `map` but `True` in `flatten_indices`. This creates unexpected behaviors like this ```python from datasets import Dataset, concatenate_datasets d1 = Dataset.from_dict({"a": [0, 1, 2, 3]}) d2 = d1.filter(lambda x: x["a"] < 2).flatten_indices() d1.data.schema == d2.data.schema # False ``` This can cause issues when concatenating datasets for example. For consistency I set `disable_nullable` to `False` in `flatten_indices` and I fixed some docstrings cc @SBrandeis
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
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[ "Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?\r\n\r\nMaybe you're having this error because you don't have access to this URL from python ?" ]
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when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?
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Unpin keras once TF fixes its release
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Related to: - #3208
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Pin keras version until TF fixes its release
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Fix #3207.
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CI error: Another metric with the same name already exists in Keras 2.7.0
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## Describe the bug Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See: - keras-team/keras#15579 This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363
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